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Approximate nearest-neighbor search (ANNS) algorithms are a key part of the modern deep learning stack due to enabling efficient similarity search over high-dimensional vector space representations (i.e., embeddings) of data.
Streaming Similarity Search over one Billion Tweets using Parallel Locality-Sensitive Hashing
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Hnswlib - fast approximate nearest neighbor search
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HVS: Hierarchical Graph Structure Based on Voronoi Diagrams for Solving Approximate Nearest Neighbor Search
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Speed-ANN: Low-Latency and High-Accuracy Nearest Neighbor Search via Intra-Query Parallelism
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ANN-Benchmarks: A benchmarking tool for approximate nearest neighbor algorithms
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Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs
Yury A. Malkov and Dmitry A. Yashunin. 2020 · 2020
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Many Sequential Iterative Algorithms Can Be Parallel and (Nearly) Work-efficient. In ACM Symposium on Parallelism in Algorithms and Architectures (SPAA)
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